Expanding the language model in a low-resource hybrid MT system

Abstract

The present article investigates the fusion of different language models to improve translation accuracy. A hybrid MT system, recently-developed in the European Commission-funded PRESEMT project that combines example-based MT and Statistical MT principles is used as a starting point. In this article, the syntactically-defined phrasal language models (NPs, VPs etc.) used by this MT system are supplemented by n-gram language models to improve translation accuracy. For specific structural patterns, n-gram statistics are consulted to determine whether the pattern instantiations are corroborated. Experiments indicate improvements in translation accuracy.